Using AI for HealthcareSomeone has said the Data is the new oil for this century. What oil did to move humanity forward over the last 100 years, the same is projected for data. Healthcare maybe late to the party but there is plenty of party left! Below is results published by McKinsey on potential savings due to AI and Machine learning in healthcare in US and UK.
|Source: McKinsey Global Institute|
As can be seen the savings are projected to be substantial. Institutions will need to reframe the way they think about big data that will become increasing vital for cutting costs and improving outcomes: what types of data are useful, how data should be shared, and how data based algorithms can be designed to out-perform humans.
AI has already made inroads in areas such radiology, drug research, workflow optimization and more. As more authentic and clean data is freed from data silos where in many cases it is trapped, more AI applications can be unleashed. We discussed the issues and ways to bust data silos in post: Unleashing Healthcare data from Silos
Even though we are just getting started on making use of AI and ML in healthcare, a word of caution. There is no magic cure for what ails healthcare, and no single solution for turning the healthcare system into the equivalent of a well oiled wellness dispensing machine.
Instead, changes will happen step by step, starting with how this first generation of artificial intelligence tools while we address fundamental challenges such as data discovery and access, algorithmic development, and changing attitudes.
What’s next?Artificial intelligence goes hand-in-hand with machine learning, natural language processing and other technologies, all of which can be combined to process the huge amounts of big data that we create on a daily basis. In the healthcare industry, being able to process this data and to draw new conclusions isn’t just a matter of making money — it’s a matter of improving lives of millions of patients.
Many clinical trials end in failure is no secret. But, AI can play a vital role in analyzing results from failed trials to help sponsors uncover insights and patterns that can help improve design and management of future trials.
Because AI methods have only begun to be applied to clinical trials in the past 5 to 6 years, it will most likely be another few years before AI's full impact can be accurately assessed.